Deep Successive Subspace Learning for Data Clustering

Abstract

Deep clustering combines embedding and clustering together to obtain an optimal low dimensional embedding sub- space (aka latent subspace) for clustering, which can be more effective compared to conventional clustering approaches such as k-means. Typical deep clustering methods employ autoencoder (AE) and obtain their optimal latent space through minimizing data reconstruction loss which has no substantial connection with the clustering performance. In contrast, in this paper we propose a novel AE-based clustering scheme Deep Successive Subspace Learning (DSSL) which simultaneously minimizes weighted reconstruction and clustering losses of data points, where weights are defined based on similarity between latent representation of data points and cluster centers. DSSL obtains its optimal latent space through K (i.e. number of clusters) successive training runs where each run corresponds to an individual cluster. At each run, DSSL focuses on reconstruction and clustering of those data points that are more likely to belong to the corresponding cluster; hence, implicitly training those network parameters that have more influence on that cluster. Experimental results on benchmark datasets demonstrate that the proposed DSSL method can significantly outperform state-of-the-art clustering approaches.